Abstract

Robotics can serve as a testbed for cognitive theories. One behavioral criterion for comparing theories is the extent to which their implementations can learn to exploit new environmental opportunities. Furthermore, a robotics testbed forces researcher to confront fundamental issues concerning how internal representations are grounded in activity. In our approach, a mobile robot takes the role of a creature that must survive in an unknown environment. The robot has no a priori knowledge about what constitutes a suitable goal --- what is edible, inedible, or dangerous --- or even its shape or how its body works. Nevertheless, the robot learns how to survive. The robot does this by tracking segmented regions of its camera image while moving. The robot projects these regions into a canonical wavelet domain that highlights color and intensity changes at various scales. This reveals sensory invariance that is readily extracted with Bayesian statistics. The robot simultaneously learns an ad...

Citations